Determining the potato samples processed manner with an electronic nose system supported by a machine-learning approach

Abstract

Through several studies, electronic nose (E-nose) devices have been demonstrated to serve as useful measurement equipment for the fast and reliable analysis of complex odour profiles in a wide range of fields, including the area of ensuring food safety. In the same perspective, i.e. checking food safety comes this work, where potatoes are assessed using an E-nose principally made of five metal oxide gas sensors supported with a machine learning algorithm, said evaluation is based on their origin fields: naturally treated, treated with chemical NPK fertilizers, or treated with chicken manure. The technique of support vector machines has been exploited for the data obtained from the multi-sensor system in order to distinguish the potato types mentioned by following two methods: a direct method where all the data have been exploited with a rate of success of 91.7%, and a composed method where the classification was first between naturally treated samples and those treated differently with a success rate of 96.7%, then a classification between those treated differently had a 92.5% success rate. A microbiological analysis was also carried out and its results were compared with those obtained by the E-nose. As per the two methods’ results, the distinction of the potato types by the nature of the cultivated fields is possible with the recording of the multisensory system superiority due to response time, low cost, simplicity, and portability.